{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ba430d2400>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.4864 - accuracy: 0.8245 - val_loss: 0.4590 - val_accuracy: 0.8335\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.3672 - accuracy: 0.8636 - val_loss: 0.4221 - val_accuracy: 0.8442\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 3s 43us/sample - loss: 0.3354 - accuracy: 0.8759 - val_loss: 0.3738 - val_accuracy: 0.8599\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.3073 - accuracy: 0.8860 - val_loss: 0.3675 - val_accuracy: 0.8686\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 3s 43us/sample - loss: 0.2906 - accuracy: 0.8915 - val_loss: 0.3438 - val_accuracy: 0.8763\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.2775 - accuracy: 0.8959 - val_loss: 0.3541 - val_accuracy: 0.8751\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.2645 - accuracy: 0.9001 - val_loss: 0.3365 - val_accuracy: 0.8802\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 3s 43us/sample - loss: 0.2548 - accuracy: 0.9037 - val_loss: 0.3227 - val_accuracy: 0.8861\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.2441 - accuracy: 0.9067 - val_loss: 0.3552 - val_accuracy: 0.8792\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 3s 45us/sample - loss: 0.2361 - accuracy: 0.9105 - val_loss: 0.3350 - val_accuracy: 0.8844\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.2269 - accuracy: 0.9131 - val_loss: 0.3375 - val_accuracy: 0.8819\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.2209 - accuracy: 0.9160 - val_loss: 0.3730 - val_accuracy: 0.8748\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.2117 - accuracy: 0.9179 - val_loss: 0.3480 - val_accuracy: 0.8823\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 3s 45us/sample - loss: 0.2078 - accuracy: 0.9195 - val_loss: 0.3454 - val_accuracy: 0.8854\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 3s 45us/sample - loss: 0.2020 - accuracy: 0.9219 - val_loss: 0.3516 - val_accuracy: 0.8886\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.1956 - accuracy: 0.9244 - val_loss: 0.3291 - val_accuracy: 0.8910\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.1894 - accuracy: 0.9256 - val_loss: 0.3475 - val_accuracy: 0.8887\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 3s 45us/sample - loss: 0.1854 - accuracy: 0.9287 - val_loss: 0.3728 - val_accuracy: 0.8842\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s 47us/sample - loss: 0.1818 - accuracy: 0.9312 - val_loss: 0.3727 - val_accuracy: 0.8875\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 3s 45us/sample - loss: 0.1759 - accuracy: 0.9321 - val_loss: 0.3980 - val_accuracy: 0.8883\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_images, \n",
    "                    train_labels, \n",
    "                    epochs=20, \n",
    "                    validation_data=(test_images, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 22us/sample - loss: 0.3980 - accuracy: 0.8883\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ba3f624f60>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history['loss'], 'r', label='loss')\n",
    "plt.plot(history.epoch, history.history['val_loss'], 'b--', label='val_loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ba3f8537b8>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history['accuracy'], 'r')\n",
    "plt.plot(history.epoch, history.history['val_accuracy'], 'b--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
